Fay’s BRR Method

Fay’s method is a modification of the BRR method, and it requires a stratified sample design with two primary sampling units (PSUs) per stratum. The total number of
replicates R is the smallest multiple of 4 that is greater than the total number of strata H. However, if you prefer a larger number of replicates, you can specify the REPS=method-option.

For each replicate, Fay’s method uses a Fay coefficient to impose a perturbation of the original weights in the full sample that is gentler than using only half-samples, as in the
traditional BRR method. The Fay coefficient can be set by specifying the FAY = method-option. By default, if the FAYmethod-option is specified without providing a value for (Judkins, 1990; Rao and Shao, 1999). When , Fay’s method becomes the traditional BRR method. For more details, see Dippo, Fay, and Morganstein (1984); Fay (1984, 1989); Judkins (1990).

Let H be the number of strata. Replicates are constructed by using the first H columns of the Hadamard matrix, where R is the number of replicates, . The rth () replicate is created from the full sample according to the rth row of the Hadamard matrix as follows:

If the element of the Hadamard matrix is 1, then the full sample weight of the first PSU in stratum h is multiplied by and the full sample weight of the second PSU is multiplied by to obtain the rth replicate weights.

If the element of the Hadamard matrix is –1, then the full sample weight of the first PSU in stratum h is multiplied by and the full sample weight of the second PSU is multiplied by to obtain the rth replicate weights.

By default, an appropriate Hadamard matrix is generated automatically to create the replicates. You can request that the Hadamard
matrix be displayed by specifying the VARMETHOD=BRR(PRINTH)method-option. If you provide a Hadamard matrix by specifying the VARMETHOD=BRR(HADAMARD=)method-option, then the replicates are generated according to the provided Hadamard matrix.

Let be the estimated regression coefficients from the full sample for . Let be the estimated regression coefficient obtained from the rth replicate by using replicate weights. PROC SURVEYREG estimates the covariance matrix of by